Invariant Features from Interest Point Groups (original) (raw)

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision, 2004

This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

On the Improvement of Image Feature Matching under Perspective Transformations

2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images, 2010

This paper presents a novel methodology to perform consistent matching between visual features of a pair of images, particularly in the case of point-of-view changes between shots. Traditionally, such correspondences are determined by computing the similarity between descriptor vectors associated with each point which are obtained by invariant descriptors. Our methodology first obtains a coarse global registration among images, which constrains the correspondence space. Then, it analyzes the similarity among descriptors, thus reducing both the number and the severity of mismatches. The approach is sufficiently generic to be used with many feature descriptor methods. We present several experimental results that show significant increase both in accuracy and the number of successful matches.

Multi-Image Matching Using Multi-Scale Oriented Patches

2005

This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 × 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.

Towards a Robust Scale Invariant Feature Correspondence

Lecture Notes in Computer Science, 2015

In this paper, we introduce an improved scale invariant feature correspondence algorithm which depends on the Similarity-Topology Matching algorithm. It pays attention not only to the similarity between features but also to the spatial layout of every matched feature and its neighbours. The features are represented as an undirected graph where every node represents a local feature and every edge represents adjacency between them. The topology of the resulting graph can be considered as a robust global feature of the represented object. The matching process is modeled as a graph matching problem; which in turn is formulated as a variation of the quadratic assignment problem. The Similarity-Topology Matching algorithm achieves superior performance in almost all the experiments except when the image has been exposed to scaling deformations. An amendment has been done to the algorithm in order to cope with this limitation. In this work, we depend not only on the distance between the two interest points but also on the scale at which the interest points are detected to decide the neighbourhood relations between every pair of features. A set of challenging experiments conducted using 50 images (contain repeated structure) representing 5 objects from COIL-100 data-set with extra synthetic deformations reveal that the modified version of the Similarity-Topology Matching algorithm has better performance. It is considered more robust especially under the scale deformations.

A framework for efficient correspondence using feature interrelations

2008 19th International Conference on Pattern Recognition, 2008

We propose a formulation for solving the point pattern correspondence problem, relying on transformation invariants. Our approach can accommodate any degree of descriptors thus modeling any kind of potential deformation according to the needs of each specific problem. Other potential descriptors such as color or local appearance can also be incorporated. A brief study on the complexity of the methodology is made which proves to be inherently polynomial while allowing for further adjustments via thresholding. Initial experiments on both synthetic and real data demonstrate its potentials in terms of accuracy and robustness to noise and outliers.

Reducing ambiguity in feature point matching by preserving local geometric consistency

2008 15th IEEE International Conference on Image Processing, 2008

In this paper, feature point matching is formulated as an optimization problem in which the uniqueness condition is constrained. We propose a novel score function based on homography-induced pairwise constraints, and a novel optimization algorithm based on relaxation labeling. Homographyinduced pairwise constraints are effective for image pairs with viewpoint or scale changes, unlike previous pairwise constraints. The proposed optimization algorithm searches for a uniqueness-constrained solution, while the original relaxation-labeling algorithm is appropriate for finding manyto-one correspondences. The effectiveness of the proposed method is shown by experiments involving image pairs with viewpoint or scale changes in addition to repeated textures and nonrigid deformation. The proposed method is also applied to object recognition, giving some promising results.

RASIM: A Novel Rotation and Scale Invariant Matching of Local Image Interest Points

IEEE Transactions on Image Processing, 2011

This paper presents a novel algorithm for matching image interest points. Potential interest points are identified by searching for local peaks in Difference-of-Gaussian (DoG) images. We refine and assign rotation, scale and location for each keypoint by using the SIFT algorithm [1]. Pseudo log-polar sampling grid is then applied to properly scaled image patches around each keypoint, and a weighted adaptive lifting scheme transform is designed for each ring of the log-polar grid. The designed adaptive transform for a ring in the reference keypoint and the general non-adaptive transform are applied to the corresponding ring in a test keypoint. Similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and nonadaptive transforms. We refer to the proposed versatile system of Rotation And Scale Invariant Matching as RASIM. Our experiments show that the accuracy of RASIM is more than SIFT, which is the most widely used interest point matching algorithm in the literature. RASIM is also more robust to image deformations while its computation time is comparable to SIFT.

Using Local Affine Invariants to Improve Image Matching

2010 20th International Conference on Pattern Recognition, 2010

A method to classify tentative feature matches as inliers or outliers to a transformation model is presented. It is well known that ratios of areas of corresponding shapes are affine invariants [6]. Our algorithm uses consistency of ratios of areas in pairs of images to classify matches as inliers or outliers. The method selects four matches within a region, and generates all possible corresponding triangles. All matches are classified as inliers or outliers based on the variance among the ratio of areas of the triangles. The selected inliers are used to compute a homography transformation. We present experimental results showing significant improvements over the baseline RANSAC algorithm for pairs of images from the Zurich Building Database [5].

Keypoint descriptor matching with context-based orientation estimation

This paper presents a matching strategy to improve the discriminative power of histogram-based keypoint descriptors by constraining the range of allowable dominant orientations according to the context of the scene under observation. This can be done when the descriptor uses a circular grid and quantized orientation steps, by computing or providing a global reference orientation based on the feature matches.

Keypoint descriptor matching with context-based orientation estimation–Additional material–

Invariant Features from Interest Point Groups (original) (raw)

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